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==Factor interpretation==
Factor loadings are numerical values that indicate the strength and direction of a factor on a measured variable. Factor loadings indicate how strongly the factor influences the measured variable. In order to label the factors in the model, researchers should examine the factor pattern to see which items load highly on which factors and then determine what those items have in common.<ref name =Fabrigar/> Whatever the items have in common will indicate the meaning of the factor.
However, while exploratory factor analysis is a powerful tool for uncovering underlying structures among variables, it is crucial to avoid reliance on it without adequate theorizing. Armstrong's<ref>{{cite journal |last1=Armstrong |first1=J. Scott |title=Derivation of Theory by Means of Factor Analysis or Tom Swift and His Electric Factor Analysis Machine |journal=The American Statistician |date=December 1967 |volume=21 |issue=5 |pages=17–21 |doi=10.1080/00031305.1967.10479849}}</ref> critique highlights that EFA, when conducted without a theoretical framework, can lead to misleading interpretations. For instance, in a hypothetical case study involving the analysis of various physical properties of metals, the results of EFA failed to identify the true underlying factors, instead producing an "over-factored" model that obscured the simplicity of the relationships amongst the observed variables.
==See also==
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